Overview

Dataset statistics

Number of variables20
Number of observations11933
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

weight2 is highly overall correlated with htin4 and 3 other fieldsHigh correlation
height3 is highly overall correlated with htin4 and 1 other fieldsHigh correlation
alcday5 is highly overall correlated with x.drnkwekHigh correlation
htin4 is highly overall correlated with weight2 and 2 other fieldsHigh correlation
wtkg3 is highly overall correlated with weight2 and 1 other fieldsHigh correlation
x.bmi5 is highly overall correlated with weight2 and 1 other fieldsHigh correlation
htm4 is highly overall correlated with weight2 and 2 other fieldsHigh correlation
x.drnkwek is highly overall correlated with alcday5 and 1 other fieldsHigh correlation
drocdy3. is highly overall correlated with x.drnkwekHigh correlation
x.llcpwt2 is highly overall correlated with x.llcpwt and 2 other fieldsHigh correlation
x.llcpwt is highly overall correlated with x.llcpwt2 and 2 other fieldsHigh correlation
x.wt2rake is highly overall correlated with x.llcpwt2 and 2 other fieldsHigh correlation
x.strwt is highly overall correlated with x.llcpwt2 and 2 other fieldsHigh correlation
physhlth is highly overall correlated with menthlthHigh correlation
menthlth is highly overall correlated with physhlthHigh correlation
x.drnkwek has 5546 (46.5%) zerosZeros
drocdy3. has 5546 (46.5%) zerosZeros

Reproduction

Analysis started2022-12-09 00:37:41.474174
Analysis finished2022-12-09 00:38:40.964973
Duration59.49 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

weight2
Real number (ℝ)

Distinct255
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.39697
Minimum50
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:41.090583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile120
Q1150
median180
Q3200
95-th percentile260
Maximum390
Range340
Interquartile range (IQR)50

Descriptive statistics

Standard deviation43.004842
Coefficient of variation (CV)0.23839005
Kurtosis1.4302054
Mean180.39697
Median Absolute Deviation (MAD)27
Skewness0.87560842
Sum2152677
Variance1849.4164
MonotonicityNot monotonic
2022-12-08T16:38:41.243336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 1395
 
11.7%
200 535
 
4.5%
150 514
 
4.3%
160 493
 
4.1%
170 436
 
3.7%
140 376
 
3.2%
190 369
 
3.1%
175 310
 
2.6%
165 303
 
2.5%
130 292
 
2.4%
Other values (245) 6910
57.9%
ValueCountFrequency (%)
50 1
 
< 0.1%
51 1
 
< 0.1%
60 1
 
< 0.1%
70 3
< 0.1%
75 1
 
< 0.1%
77 1
 
< 0.1%
80 2
 
< 0.1%
85 6
0.1%
86 1
 
< 0.1%
87 1
 
< 0.1%
ValueCountFrequency (%)
390 6
0.1%
386 1
 
< 0.1%
382 1
 
< 0.1%
380 5
< 0.1%
372 1
 
< 0.1%
371 1
 
< 0.1%
370 3
< 0.1%
365 1
 
< 0.1%
360 6
0.1%
358 1
 
< 0.1%

height3
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean506.1428
Minimum500
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:41.389621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile501
Q1504
median507
Q3508
95-th percentile511
Maximum511
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7642702
Coefficient of variation (CV)0.0054614432
Kurtosis-0.48083327
Mean506.1428
Median Absolute Deviation (MAD)2
Skewness-0.31990751
Sum6039802
Variance7.6411895
MonotonicityNot monotonic
2022-12-08T16:38:41.535876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
507 3479
29.2%
506 1050
 
8.8%
504 966
 
8.1%
505 922
 
7.7%
508 912
 
7.6%
503 846
 
7.1%
510 797
 
6.7%
509 786
 
6.6%
502 726
 
6.1%
511 678
 
5.7%
Other values (2) 771
 
6.5%
ValueCountFrequency (%)
500 384
 
3.2%
501 387
 
3.2%
502 726
 
6.1%
503 846
 
7.1%
504 966
 
8.1%
505 922
 
7.7%
506 1050
 
8.8%
507 3479
29.2%
508 912
 
7.6%
509 786
 
6.6%
ValueCountFrequency (%)
511 678
 
5.7%
510 797
 
6.7%
509 786
 
6.6%
508 912
 
7.6%
507 3479
29.2%
506 1050
 
8.8%
505 922
 
7.7%
504 966
 
8.1%
503 846
 
7.1%
502 726
 
6.1%

children
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.810442
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:41.649203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)83

Descriptive statistics

Standard deviation37.732501
Coefficient of variation (CV)0.57335128
Kurtosis-0.78437867
Mean65.810442
Median Absolute Deviation (MAD)0
Skewness-1.1004611
Sum785316
Variance1423.7416
MonotonicityNot monotonic
2022-12-08T16:38:41.763255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
88 8768
73.5%
1 1239
 
10.4%
2 1141
 
9.6%
3 420
 
3.5%
4 179
 
1.5%
99 77
 
0.6%
5 71
 
0.6%
6 23
 
0.2%
7 7
 
0.1%
9 4
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1239
10.4%
2 1141
9.6%
3 420
 
3.5%
4 179
 
1.5%
5 71
 
0.6%
6 23
 
0.2%
7 7
 
0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
99 77
 
0.6%
88 8768
73.5%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 3
 
< 0.1%
7 7
 
0.1%
6 23
 
0.2%
5 71
 
0.6%
4 179
 
1.5%
3 420
 
3.5%

alcday5
Real number (ℝ)

Distinct38
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541.51094
Minimum101
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:41.891762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1202
median888
Q3888
95-th percentile888
Maximum999
Range898
Interquartile range (IQR)686

Descriptive statistics

Standard deviation356.64686
Coefficient of variation (CV)0.65861432
Kurtosis-1.9503956
Mean541.51094
Median Absolute Deviation (MAD)0
Skewness-0.078043159
Sum6461850
Variance127196.98
MonotonicityNot monotonic
2022-12-08T16:38:42.025745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
888 5982
50.1%
201 901
 
7.6%
202 646
 
5.4%
101 471
 
3.9%
102 435
 
3.6%
230 412
 
3.5%
203 361
 
3.0%
205 311
 
2.6%
204 307
 
2.6%
103 301
 
2.5%
Other values (28) 1806
 
15.1%
ValueCountFrequency (%)
101 471
3.9%
102 435
3.6%
103 301
 
2.5%
104 126
 
1.1%
105 145
 
1.2%
106 39
 
0.3%
107 204
 
1.7%
201 901
7.6%
202 646
5.4%
203 361
3.0%
ValueCountFrequency (%)
999 44
 
0.4%
888 5982
50.1%
777 99
 
0.8%
230 412
 
3.5%
229 14
 
0.1%
228 27
 
0.2%
227 6
 
0.1%
226 5
 
< 0.1%
225 78
 
0.7%
224 8
 
0.1%

x.psu
Real number (ℝ)

Distinct7775
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0180058 × 109
Minimum2.018 × 109
Maximum2.0180357 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:42.192078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.018 × 109
5-th percentile2.0180004 × 109
Q12.0180021 × 109
median2.0180043 × 109
Q32.0180076 × 109
95-th percentile2.0180159 × 109
Maximum2.0180357 × 109
Range35720
Interquartile range (IQR)5539

Descriptive statistics

Standard deviation5695.2278
Coefficient of variation (CV)2.8222059 × 10-6
Kurtosis7.3621435
Mean2.0180058 × 109
Median Absolute Deviation (MAD)2535
Skewness2.3828154
Sum2.4080863 × 1013
Variance32435620
MonotonicityNot monotonic
2022-12-08T16:38:42.350554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018003506 8
 
0.1%
2018001343 8
 
0.1%
2018000983 6
 
0.1%
2018004994 6
 
0.1%
2018003451 6
 
0.1%
2018000065 6
 
0.1%
2018000102 6
 
0.1%
2018001290 6
 
0.1%
2018002705 6
 
0.1%
2018004047 6
 
0.1%
Other values (7765) 11869
99.5%
ValueCountFrequency (%)
2018000001 1
 
< 0.1%
2018000002 4
< 0.1%
2018000003 3
< 0.1%
2018000004 1
 
< 0.1%
2018000005 4
< 0.1%
2018000006 2
< 0.1%
2018000007 4
< 0.1%
2018000008 1
 
< 0.1%
2018000009 4
< 0.1%
2018000010 2
< 0.1%
ValueCountFrequency (%)
2018035721 1
< 0.1%
2018035620 1
< 0.1%
2018035563 1
< 0.1%
2018035477 1
< 0.1%
2018035474 1
< 0.1%
2018035409 1
< 0.1%
2018035373 1
< 0.1%
2018035338 1
< 0.1%
2018035305 1
< 0.1%
2018035267 1
< 0.1%

physhlth
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.782955
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:42.518185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q115
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)73

Descriptive statistics

Standard deviation36.945103
Coefficient of variation (CV)0.60782013
Kurtosis-1.4091911
Mean60.782955
Median Absolute Deviation (MAD)0
Skewness-0.69240404
Sum725323
Variance1364.9407
MonotonicityNot monotonic
2022-12-08T16:38:42.642263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
88 7424
62.2%
30 962
 
8.1%
2 649
 
5.4%
1 498
 
4.2%
3 373
 
3.1%
5 333
 
2.8%
10 244
 
2.0%
15 226
 
1.9%
77 224
 
1.9%
7 223
 
1.9%
Other values (22) 777
 
6.5%
ValueCountFrequency (%)
1 498
4.2%
2 649
5.4%
3 373
3.1%
4 194
 
1.6%
5 333
2.8%
6 63
 
0.5%
7 223
 
1.9%
8 34
 
0.3%
9 10
 
0.1%
10 244
 
2.0%
ValueCountFrequency (%)
99 42
 
0.4%
88 7424
62.2%
77 224
 
1.9%
30 962
 
8.1%
29 8
 
0.1%
28 22
 
0.2%
27 8
 
0.1%
26 1
 
< 0.1%
25 56
 
0.5%
24 1
 
< 0.1%

menthlth
Real number (ℝ)

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.526439
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:42.791150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q120
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)68

Descriptive statistics

Standard deviation36.210707
Coefficient of variation (CV)0.57001002
Kurtosis-1.1707878
Mean63.526439
Median Absolute Deviation (MAD)0
Skewness-0.8576702
Sum758061
Variance1311.2153
MonotonicityNot monotonic
2022-12-08T16:38:42.934651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
88 7948
66.6%
30 654
 
5.5%
2 586
 
4.9%
5 421
 
3.5%
1 348
 
2.9%
3 326
 
2.7%
10 307
 
2.6%
15 288
 
2.4%
7 190
 
1.6%
4 174
 
1.5%
Other values (23) 691
 
5.8%
ValueCountFrequency (%)
1 348
2.9%
2 586
4.9%
3 326
2.7%
4 174
 
1.5%
5 421
3.5%
6 48
 
0.4%
7 190
 
1.6%
8 42
 
0.4%
9 4
 
< 0.1%
10 307
2.6%
ValueCountFrequency (%)
99 49
 
0.4%
88 7948
66.6%
77 150
 
1.3%
30 654
 
5.5%
29 4
 
< 0.1%
28 22
 
0.2%
27 10
 
0.1%
26 2
 
< 0.1%
25 58
 
0.5%
24 3
 
< 0.1%

sleptim1
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8349116
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:43.067134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q38
95-th percentile9
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.7235093
Coefficient of variation (CV)0.9857813
Kurtosis79.608776
Mean7.8349116
Median Absolute Deviation (MAD)1
Skewness8.8168433
Sum93494
Variance59.652596
MonotonicityNot monotonic
2022-12-08T16:38:43.190686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7 3578
30.0%
8 3452
28.9%
6 2527
21.2%
5 788
 
6.6%
9 506
 
4.2%
4 343
 
2.9%
10 274
 
2.3%
77 126
 
1.1%
12 100
 
0.8%
3 100
 
0.8%
Other values (12) 139
 
1.2%
ValueCountFrequency (%)
1 36
 
0.3%
2 32
 
0.3%
3 100
 
0.8%
4 343
 
2.9%
5 788
 
6.6%
6 2527
21.2%
7 3578
30.0%
8 3452
28.9%
9 506
 
4.2%
10 274
 
2.3%
ValueCountFrequency (%)
99 9
 
0.1%
77 126
1.1%
23 1
 
< 0.1%
20 3
 
< 0.1%
18 4
 
< 0.1%
17 2
 
< 0.1%
16 8
 
0.1%
15 12
 
0.1%
14 6
 
0.1%
13 2
 
< 0.1%

htin4
Real number (ℝ)

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.951311
Minimum48
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:43.319558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile61
Q164
median67
Q370
95-th percentile74
Maximum81
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0288175
Coefficient of variation (CV)0.060175334
Kurtosis-0.19693051
Mean66.951311
Median Absolute Deviation (MAD)3
Skewness0.076357052
Sum798930
Variance16.23137
MonotonicityNot monotonic
2022-12-08T16:38:43.458399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
67 1448
12.1%
66 1050
 
8.8%
64 966
 
8.1%
65 922
 
7.7%
68 912
 
7.6%
63 846
 
7.1%
72 817
 
6.8%
70 797
 
6.7%
69 786
 
6.6%
62 726
 
6.1%
Other values (23) 2663
22.3%
ValueCountFrequency (%)
48 5
 
< 0.1%
49 1
 
< 0.1%
50 2
 
< 0.1%
51 1
 
< 0.1%
53 2
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
56 10
 
0.1%
57 22
0.2%
58 46
0.4%
ValueCountFrequency (%)
81 2
 
< 0.1%
80 6
 
0.1%
79 5
 
< 0.1%
78 22
 
0.2%
77 41
 
0.3%
76 110
 
0.9%
75 160
 
1.3%
74 271
 
2.3%
73 399
3.3%
72 817
6.8%

wtkg3
Real number (ℝ)

Distinct286
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8181.474
Minimum2268
Maximum22906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:43.630262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2268
5-th percentile5443
Q16804
median7938
Q39072
95-th percentile11793
Maximum22906
Range20638
Interquartile range (IQR)2268

Descriptive statistics

Standard deviation2000.1837
Coefficient of variation (CV)0.24447718
Kurtosis2.57045
Mean8181.474
Median Absolute Deviation (MAD)1134
Skewness1.0766844
Sum97629529
Variance4000734.6
MonotonicityNot monotonic
2022-12-08T16:38:43.788661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7938 1092
 
9.2%
8165 564
 
4.7%
9072 535
 
4.5%
6804 514
 
4.3%
7257 493
 
4.1%
7711 436
 
3.7%
6350 376
 
3.2%
8618 369
 
3.1%
7484 303
 
2.5%
5897 292
 
2.4%
Other values (276) 6959
58.3%
ValueCountFrequency (%)
2268 1
 
< 0.1%
2313 1
 
< 0.1%
2722 1
 
< 0.1%
3175 3
< 0.1%
3402 1
 
< 0.1%
3493 1
 
< 0.1%
3629 2
 
< 0.1%
3856 6
0.1%
3901 1
 
< 0.1%
3946 1
 
< 0.1%
ValueCountFrequency (%)
22906 1
< 0.1%
22816 1
< 0.1%
20412 1
< 0.1%
19958 2
< 0.1%
19504 1
< 0.1%
19278 1
< 0.1%
19187 1
< 0.1%
18824 1
< 0.1%
18688 1
< 0.1%
18597 1
< 0.1%

x.bmi5
Real number (ℝ)

Distinct1496
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2799.0185
Minimum1331
Maximum5052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:43.975069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1331
5-th percentile2022
Q12428
median2726
Q33072
95-th percentile3897
Maximum5052
Range3721
Interquartile range (IQR)644

Descriptive statistics

Standard deviation563.41019
Coefficient of variation (CV)0.20128848
Kurtosis1.2997788
Mean2799.0185
Median Absolute Deviation (MAD)317
Skewness0.94664942
Sum33400688
Variance317431.04
MonotonicityNot monotonic
2022-12-08T16:38:44.120931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2726 1079
 
9.0%
2663 123
 
1.0%
2744 92
 
0.8%
2441 91
 
0.8%
2712 82
 
0.7%
2746 79
 
0.7%
2510 78
 
0.7%
2929 77
 
0.6%
2834 72
 
0.6%
2421 64
 
0.5%
Other values (1486) 10096
84.6%
ValueCountFrequency (%)
1331 1
< 0.1%
1356 1
< 0.1%
1364 1
< 0.1%
1367 1
< 0.1%
1406 1
< 0.1%
1414 1
< 0.1%
1459 1
< 0.1%
1469 1
< 0.1%
1477 2
< 0.1%
1492 1
< 0.1%
ValueCountFrequency (%)
5052 2
< 0.1%
5049 1
 
< 0.1%
5048 1
 
< 0.1%
5040 1
 
< 0.1%
5021 2
< 0.1%
5012 1
 
< 0.1%
4995 1
 
< 0.1%
4992 4
< 0.1%
4989 1
 
< 0.1%
4980 1
 
< 0.1%

htm4
Real number (ℝ)

Distinct47
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.02866
Minimum122
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:44.284235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile155
Q1163
median170
Q3178
95-th percentile188
Maximum206
Range84
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.29852
Coefficient of variation (CV)0.06056932
Kurtosis-0.16192213
Mean170.02866
Median Absolute Deviation (MAD)7
Skewness0.055583848
Sum2028952
Variance106.05952
MonotonicityNot monotonic
2022-12-08T16:38:44.427077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
170 1399
11.7%
168 1052
 
8.8%
163 969
 
8.1%
165 923
 
7.7%
173 913
 
7.7%
160 852
 
7.1%
183 817
 
6.8%
178 799
 
6.7%
175 787
 
6.6%
157 727
 
6.1%
Other values (37) 2695
22.6%
ValueCountFrequency (%)
122 5
 
< 0.1%
124 1
 
< 0.1%
127 2
 
< 0.1%
130 2
 
< 0.1%
135 2
 
< 0.1%
137 1
 
< 0.1%
140 3
 
< 0.1%
142 10
 
0.1%
145 22
0.2%
147 46
0.4%
ValueCountFrequency (%)
206 2
 
< 0.1%
205 1
 
< 0.1%
203 6
 
0.1%
201 5
 
< 0.1%
198 22
 
0.2%
196 41
 
0.3%
193 110
 
0.9%
191 160
1.3%
188 271
2.3%
185 399
3.3%

x.drnkwek
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct104
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6358.6454
Minimum0
Maximum99900
Zeros5546
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:44.589744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q3400
95-th percentile99900
Maximum99900
Range99900
Interquartile range (IQR)400

Descriptive statistics

Standard deviation23843.214
Coefficient of variation (CV)3.7497317
Kurtosis11.444856
Mean6358.6454
Median Absolute Deviation (MAD)23
Skewness3.6640616
Sum75877715
Variance5.6849887 × 108
MonotonicityNot monotonic
2022-12-08T16:38:44.757187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5546
46.5%
99900 727
 
6.1%
23 624
 
5.2%
47 480
 
4.0%
700 321
 
2.7%
93 313
 
2.6%
200 251
 
2.1%
100 239
 
2.0%
140 225
 
1.9%
1400 224
 
1.9%
Other values (94) 2983
25.0%
ValueCountFrequency (%)
0 5546
46.5%
23 624
 
5.2%
47 480
 
4.0%
70 209
 
1.8%
93 313
 
2.6%
100 239
 
2.0%
117 131
 
1.1%
140 225
 
1.9%
163 30
 
0.3%
187 140
 
1.2%
ValueCountFrequency (%)
99900 727
6.1%
42000 1
 
< 0.1%
26600 1
 
< 0.1%
21000 3
 
< 0.1%
19600 1
 
< 0.1%
15400 1
 
< 0.1%
14700 1
 
< 0.1%
14000 1
 
< 0.1%
10500 1
 
< 0.1%
9000 1
 
< 0.1%

drocdy3.
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.394369
Minimum0
Maximum900
Zeros5546
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:44.927940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q329
95-th percentile100
Maximum900
Range900
Interquartile range (IQR)29

Descriptive statistics

Standard deviation191.74502
Coefficient of variation (CV)3.2283368
Kurtosis14.938127
Mean59.394369
Median Absolute Deviation (MAD)3
Skewness4.070848
Sum708753
Variance36766.154
MonotonicityNot monotonic
2022-12-08T16:38:45.052169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 5546
46.5%
3 901
 
7.6%
7 646
 
5.4%
100 616
 
5.2%
900 579
 
4.9%
14 471
 
3.9%
29 435
 
3.6%
10 361
 
3.0%
17 311
 
2.6%
13 307
 
2.6%
Other values (24) 1760
 
14.7%
ValueCountFrequency (%)
0 5546
46.5%
3 901
 
7.6%
7 646
 
5.4%
10 361
 
3.0%
13 307
 
2.6%
14 471
 
3.9%
17 311
 
2.6%
20 139
 
1.2%
23 79
 
0.7%
27 80
 
0.7%
ValueCountFrequency (%)
900 579
4.9%
100 616
5.2%
97 14
 
0.1%
93 27
 
0.2%
90 6
 
0.1%
87 5
 
< 0.1%
86 39
 
0.3%
83 78
 
0.7%
80 8
 
0.1%
77 1
 
< 0.1%

x.llcpwt2
Real number (ℝ)

Distinct4090
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean598.49875
Minimum5.074045
Maximum18975.737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:45.209335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.074045
5-th percentile31.393872
Q1133.65209
median306.53051
Q3698.66744
95-th percentile2105.6394
Maximum18975.737
Range18970.663
Interquartile range (IQR)565.01535

Descriptive statistics

Standard deviation916.07612
Coefficient of variation (CV)1.5306233
Kurtosis59.006036
Mean598.49875
Median Absolute Deviation (MAD)218.41357
Skewness5.6192901
Sum7141885.6
Variance839195.46
MonotonicityNot monotonic
2022-12-08T16:38:45.428248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202.854322 103
 
0.9%
347.364408 101
 
0.8%
591.664273 87
 
0.7%
633.838616 71
 
0.6%
200.956964 58
 
0.5%
731.932005 57
 
0.5%
188.201511 50
 
0.4%
362.747245 49
 
0.4%
191.502356 47
 
0.4%
344.115404 45
 
0.4%
Other values (4080) 11265
94.4%
ValueCountFrequency (%)
5.074045 1
 
< 0.1%
5.726045 1
 
< 0.1%
8.265919 1
 
< 0.1%
8.554491 1
 
< 0.1%
8.849872 1
 
< 0.1%
9.104332 3
< 0.1%
9.394153 1
 
< 0.1%
9.444705 2
< 0.1%
9.535264 2
< 0.1%
10.466459 1
 
< 0.1%
ValueCountFrequency (%)
18975.73716 1
 
< 0.1%
16755.01932 1
 
< 0.1%
15796.41843 1
 
< 0.1%
14916.48982 1
 
< 0.1%
13139.4659 1
 
< 0.1%
11053.15498 9
0.1%
10511.79421 1
 
< 0.1%
9091.844086 1
 
< 0.1%
9009.12549 1
 
< 0.1%
8573.237046 1
 
< 0.1%

x.llcpwt
Real number (ℝ)

Distinct11577
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean602.64065
Minimum1.586053
Maximum33255.885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:45.600727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.586053
5-th percentile21.727396
Q1101.63766
median260.04018
Q3642.13848
95-th percentile2234.7532
Maximum33255.885
Range33254.299
Interquartile range (IQR)540.50081

Descriptive statistics

Standard deviation1181.6498
Coefficient of variation (CV)1.9607868
Kurtosis146.29024
Mean602.64065
Median Absolute Deviation (MAD)196.44607
Skewness8.856367
Sum7191310.8
Variance1396296.3
MonotonicityNot monotonic
2022-12-08T16:38:45.754757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158.421033 6
 
0.1%
1380.134792 4
 
< 0.1%
154.052055 4
 
< 0.1%
93.237602 4
 
< 0.1%
138.340573 4
 
< 0.1%
220.766315 4
 
< 0.1%
115.316004 3
 
< 0.1%
253.804637 3
 
< 0.1%
201.283039 3
 
< 0.1%
289.802479 3
 
< 0.1%
Other values (11567) 11895
99.7%
ValueCountFrequency (%)
1.586053 1
< 0.1%
2.284604 1
< 0.1%
2.359978 1
< 0.1%
3.317255 1
< 0.1%
3.323902 1
< 0.1%
3.526036 1
< 0.1%
3.644764 1
< 0.1%
3.709969 1
< 0.1%
3.875079 1
< 0.1%
4.04181 1
< 0.1%
ValueCountFrequency (%)
33255.88526 1
< 0.1%
30965.05685 1
< 0.1%
24090.36806 1
< 0.1%
22041.02045 1
< 0.1%
18910.35025 1
< 0.1%
18356.25929 1
< 0.1%
17154.36917 1
< 0.1%
16439.74002 1
< 0.1%
15295.79068 1
< 0.1%
14719.41168 1
< 0.1%

x.wt2rake
Real number (ℝ)

Distinct2325
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.385246
Minimum0.213384
Maximum2859.4004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:45.926746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.213384
5-th percentile3.958867
Q118.083807
median46.653717
Q389.453108
95-th percentile294.61334
Maximum2859.4004
Range2859.1871
Interquartile range (IQR)71.369301

Descriptive statistics

Standard deviation134.46228
Coefficient of variation (CV)1.6727234
Kurtosis165.77606
Mean80.385246
Median Absolute Deviation (MAD)30.394465
Skewness9.6060005
Sum959237.14
Variance18080.105
MonotonicityNot monotonic
2022-12-08T16:38:46.861598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.130293 189
 
1.6%
105.574086 165
 
1.4%
27.244899 161
 
1.3%
51.13805 153
 
1.3%
46.653717 146
 
1.2%
73.787482 122
 
1.0%
184.630736 117
 
1.0%
28.370357 83
 
0.7%
76.646341 80
 
0.7%
22.727823 75
 
0.6%
Other values (2315) 10642
89.2%
ValueCountFrequency (%)
0.213384 1
 
< 0.1%
0.370099 1
 
< 0.1%
0.387597 5
 
< 0.1%
0.426768 3
 
< 0.1%
0.458301 2
 
< 0.1%
0.463648 14
0.1%
0.51399 2
 
< 0.1%
0.574508 1
 
< 0.1%
0.638595 2
 
< 0.1%
0.667206 2
 
< 0.1%
ValueCountFrequency (%)
2859.400441 10
0.1%
2134.405202 1
 
< 0.1%
1465.724872 1
 
< 0.1%
1422.936801 1
 
< 0.1%
1362.475538 1
 
< 0.1%
1272.341332 4
 
< 0.1%
1150.965155 2
 
< 0.1%
1078.452871 1
 
< 0.1%
1043.797253 2
 
< 0.1%
985.571176 1
 
< 0.1%

x.ststr
Real number (ℝ)

Distinct1376
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299280.47
Minimum11011
Maximum722019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:47.021731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum11011
5-th percentile52059
Q1181101
median292019
Q3421042
95-th percentile532119
Maximum722019
Range711008
Interquartile range (IQR)239941

Descriptive statistics

Standard deviation157196.86
Coefficient of variation (CV)0.52524932
Kurtosis-0.69773692
Mean299280.47
Median Absolute Deviation (MAD)120000
Skewness0.13827334
Sum3.5713138 × 109
Variance2.4710854 × 1010
MonotonicityNot monotonic
2022-12-08T16:38:47.477697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132019 189
 
1.6%
362019 165
 
1.4%
272059 161
 
1.3%
412019 153
 
1.3%
272049 146
 
1.2%
242019 122
 
1.0%
241011 119
 
1.0%
722019 117
 
1.0%
152019 83
 
0.7%
271051 82
 
0.7%
Other values (1366) 10596
88.8%
ValueCountFrequency (%)
11011 7
0.1%
11012 2
 
< 0.1%
11021 8
0.1%
11022 2
 
< 0.1%
11031 5
< 0.1%
11032 1
 
< 0.1%
11041 3
 
< 0.1%
11051 8
0.1%
11061 2
 
< 0.1%
11071 7
0.1%
ValueCountFrequency (%)
722019 117
1.0%
721081 1
 
< 0.1%
721071 2
 
< 0.1%
721061 4
 
< 0.1%
721051 1
 
< 0.1%
721041 6
 
0.1%
721021 2
 
< 0.1%
721011 2
 
< 0.1%
662019 17
 
0.1%
661011 36
 
0.3%

x.strwt
Real number (ℝ)

Distinct1376
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.431925
Minimum0.387597
Maximum2859.4004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:47.724391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.387597
5-th percentile3.136146
Q115.393544
median37.187633
Q376.646341
95-th percentile283.26489
Maximum2859.4004
Range2859.0128
Interquartile range (IQR)61.252797

Descriptive statistics

Standard deviation126.99416
Coefficient of variation (CV)1.7532898
Kurtosis201.21084
Mean72.431925
Median Absolute Deviation (MAD)27.49329
Skewness10.594005
Sum864330.16
Variance16127.516
MonotonicityNot monotonic
2022-12-08T16:38:47.900513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.130293 189
 
1.6%
105.574086 165
 
1.4%
27.244899 161
 
1.3%
51.13805 153
 
1.3%
46.653717 146
 
1.2%
73.787482 122
 
1.0%
13.906822 119
 
1.0%
184.630736 117
 
1.0%
28.370357 83
 
0.7%
24.495533 82
 
0.7%
Other values (1366) 10596
88.8%
ValueCountFrequency (%)
0.387597 5
 
< 0.1%
0.426768 8
 
0.1%
0.458301 2
 
< 0.1%
0.463648 21
0.2%
0.51399 2
 
< 0.1%
0.574508 3
 
< 0.1%
0.638595 2
 
< 0.1%
0.667206 6
 
0.1%
0.740199 26
0.2%
0.757947 3
 
< 0.1%
ValueCountFrequency (%)
2859.400441 10
0.1%
1272.341332 4
 
< 0.1%
1150.965155 2
 
< 0.1%
1043.797253 2
 
< 0.1%
985.571176 1
 
< 0.1%
940.440531 10
0.1%
901.203375 2
 
< 0.1%
871.292497 2
 
< 0.1%
754.443252 7
0.1%
711.468401 3
 
< 0.1%

x.rawrake
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2591553
Minimum0.333333
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-12-08T16:38:48.052064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.333333
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5
Range4.666667
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58616842
Coefficient of variation (CV)0.46552513
Kurtosis8.2585806
Mean1.2591553
Median Absolute Deviation (MAD)0
Skewness2.5837491
Sum15025.5
Variance0.34359342
MonotonicityNot monotonic
2022-12-08T16:38:48.194775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 9227
77.3%
2 2035
 
17.1%
3 339
 
2.8%
0.5 119
 
1.0%
4 109
 
0.9%
0.666667 46
 
0.4%
5 30
 
0.3%
1.5 13
 
0.1%
0.333333 8
 
0.1%
1.333333 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0.333333 8
 
0.1%
0.5 119
 
1.0%
0.666667 46
 
0.4%
1 9227
77.3%
1.333333 3
 
< 0.1%
1.5 13
 
0.1%
1.666667 1
 
< 0.1%
2 2035
 
17.1%
2.5 3
 
< 0.1%
3 339
 
2.8%
ValueCountFrequency (%)
5 30
 
0.3%
4 109
 
0.9%
3 339
 
2.8%
2.5 3
 
< 0.1%
2 2035
 
17.1%
1.666667 1
 
< 0.1%
1.5 13
 
0.1%
1.333333 3
 
< 0.1%
1 9227
77.3%
0.666667 46
 
0.4%

Interactions

2022-12-08T16:38:37.659554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:44.373735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:47.634993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:50.332493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:53.086490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:55.705619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:58.521593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:01.244593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:04.387815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:07.039285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-08T16:38:13.826385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:16.487885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.113683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:21.785514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.121699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:27.871098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:30.720660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:33.484738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:36.305621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.167787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:46.426785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.112166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:51.836442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:54.568871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:57.204729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.015708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.198326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:05.815369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:08.513914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.181887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:13.957264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:16.619131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.253706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:21.918171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.262898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.026704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:30.866011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:33.631576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:36.453229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.283074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:46.573392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.237507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:51.960424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:54.687833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:57.337353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.158611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.318382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:05.946844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:08.673313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.297122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.102864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:16.732581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.396437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:22.050989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.391746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.161078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:30.989320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:33.774942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:36.585453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.400985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:46.704128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.381834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.093618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:54.818388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:57.473702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.291232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.442134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.082400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:08.835854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.419248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.245706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:16.858989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.522606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:22.171649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.517346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.314097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.131583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:33.915125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:36.708374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.513405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:46.829753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.527355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.217409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:54.933884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:57.623109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.415786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.565773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.218233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:08.986064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.538052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.377432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:16.986017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.650553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:22.298180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.641736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.461571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.259713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:34.058826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:36.826320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.649533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:46.969071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.658964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.357881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:55.068131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:57.768631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.558267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.702027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.348723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:09.127994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.672645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.514712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:17.119431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.779920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:22.432630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.804142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.592190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.398673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:34.195096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:36.960883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.795488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:47.098768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.791739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.507227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:55.192350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:57.919451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.687263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.824784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.473526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:09.258736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.804158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.640226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:17.247087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:19.916464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:22.590404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:25.936742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.730063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.538356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:34.353880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:37.097193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:39.936849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:47.234495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:49.919314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.642782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:55.314961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:58.100525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.835021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:03.956553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.594881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:09.404189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:11.925395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.781551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:17.371823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:20.043770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:22.721428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:26.072038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:28.865859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.683535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:34.499721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:37.230017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:40.071366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:47.368747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:50.061272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.775261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:55.447432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:58.245237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:00.980246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:04.106925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.757293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:09.535972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:12.099413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:14.926608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:17.494601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:20.184024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:23.485054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:26.210049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:29.019319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.822140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:34.647440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:37.383661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:40.214457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:47.510553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:50.206298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:52.913400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:55.578947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:37:58.386605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:01.113982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:04.253780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:06.889972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:09.674829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:12.240439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:15.073528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:17.631343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:20.324157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:23.622186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:26.344298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:29.155793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:31.953294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:34.788997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T16:38:37.522476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-08T16:38:48.388122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-08T16:38:48.700634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-08T16:38:48.973622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-08T16:38:49.235460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-08T16:38:49.536067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-08T16:38:40.439426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-08T16:38:40.810341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

weight2height3childrenalcday5x.psuphyshlthmenthlthsleptim1htin4wtkg3x.bmi5htm4x.drnkwekdrocdy3.x.llcpwt2x.llcpwtx.wt2rakex.ststrx.strwtx.rawrake
0138.0504.02.0203.020180127278888864.06260.02369.0163.014010347.364408300.33440746.65371727204946.6537171.0
1240.0507.088.0203.020180033183030772.010886.03255.0183.042010591.664273230.240437184.630736722019184.6307361.0
2120.0504.088.0888.020180146348888664.05443.02060.0163.000142.637597234.85352637.26138331219937.2613831.0
3133.0502.088.0888.020180049278888762.06033.02433.0157.000131.94350095.94418118.27113245110118.2711321.0
4170.0505.088.0105.020180055848888765.07711.02829.0165.050071155.66731387.72268913.90682224101113.9068221.0
5192.0507.088.0104.020180021818888773.08709.02533.0185.012005796.39099382.12803428.18557919101128.1855791.0
6205.0511.088.0202.020180050779999871.09299.02859.0180.04771232.7208264104.860898207.98385612049207.9838561.0
7220.0508.088.0888.020180017548830668.09979.03345.0173.000737.7071761650.56766281.87677851104140.9383892.0
8200.0507.02.0202.020180036828888773.09072.02639.0185.0477645.890548972.506855127.293033552089127.2930331.0
9200.0507.088.0777.020180106338888767.09072.03132.0170.099900900765.831083445.98036943.04830048209943.0483001.0
weight2height3childrenalcday5x.psuphyshlthmenthlthsleptim1htin4wtkg3x.bmi5htm4x.drnkwekdrocdy3.x.llcpwt2x.llcpwtx.wt2rakex.ststrx.strwtx.rawrake
11923175.0507.01.0102.02018011812288867.07938.02741.0170.040029161.234830236.67773317.40865712201917.4086571.0
11924140.0507.088.0888.020180011128888867.06350.02193.0170.00051.83063149.5193556.4968393110916.4968391.0
11925183.0507.088.0777.020180032988888673.08301.02414.0185.099900900305.178735418.97042545.9450264209945.9450261.0
11926140.0503.088.0888.020180001198888763.06350.02480.0160.000581.030702150.86158290.7884946103190.7884941.0
11927160.0500.088.0888.020180006263088960.07257.03125.0152.00063.51284676.6838647.3831624410117.3831621.0
11928250.0511.01.0107.020180033938888871.011340.03487.0180.02100100551.939252674.65686051.13805041201951.1380501.0
11929215.0510.088.0888.020180057973088870.09752.03085.0178.00065.02746098.9352615.6872222410215.6872221.0
11930160.0508.088.0888.020180034708888768.07257.02433.0173.00093.61997777.0276034.4866571020194.4866571.0
11931220.0505.088.0201.020180039518888865.09979.03661.0165.0233175.16514788.66300928.19259644201928.1925961.0
11932240.0508.088.0210.0201800452912768.010886.03649.0173.046733403.740658341.82553258.18775854201958.1877581.0